Amazon SageMaker Ground Truth provides a browser-first interface within the AWS console for orchestrating large-scale data labeling projects essential for training computer vision models. It enables ML practitioners to define annotation tasks, manage labeling workforces, and generate high-quality ground truth datasets for use cases like object detection, image classification, and semantic segmentation, directly leveraging S3 data sources and outputs.
Amazon SageMaker Ground Truth Website Full Guide (2026)
Data labeling service within AWS SageMaker.
Updated May 26, 2026

Introduction
Key Features
Core Capabilities
Image and video annotation task setup
Private, vendor, and Amazon Mechanical Turk workforce integration
Labeling job creation and management console
S3 data input/output configuration for dataset
Additional Details
Active learning for automated data labeling
Customizable labeling instructions editor
Consensus-based quality control mechanism
Bounding box, polygon, and keypoint annotation tool
Use Cases
Training Custom Object Detector
ML engineers use Ground Truth to rapidly annotate large image datasets with bounding boxes for specific objects, generating the ground truth needed to train custom YOLO or Faster R-CNN model
How to Use Amazon SageMaker Ground Truth
Configure a New Labeling Job
Navigate to the SageMaker console, select "Ground Truth" from the left navigation, and choose "Labeling jobs." Click "Create labeling job," specify your S3 input data location, and select the desired computer vision task type (e.g., "Object detection")
Amazon SageMaker Ground Truth Alternatives
Amazon Rekognition
Image and video analysis service
Microsoft Azure Computer Vision
AI services for analyzing images and video
Chooch AI
Computer vision platform offering models for various industrial applications.
Clarifai
AI platform for computer vision, NLP, and data labeling.
About Amazon SageMaker Ground Truth
Useful Links
1 totalVideo Mentions
Amazon SageMaker Ground Truth Status
Service is operational


